MAPLE : mobility support using asymmetric transmit power in low-power and lossy networks
표제/저자사항
MAPLE : mobility support using asymmetric transmit power in low-power and lossy networks / Seungbeom Jeong, Eunjeong Park, Dongyeon Woo, Hyung-Sin Kim, Jongyeup Paek, Saewoong Bahk
형태사항
p. 414-424 ; 28 cm
주기사항
수록자료: Journal of communications and networks. AIEI Korean Institute of Communication Sciences. Vol.20 No.4(2018 August), p. 414-424 20:4<414 ISSN 1229-2370↔ 저자: Seungbeom Jeong, Department of Electrical Engineering and Computer Science, INMC, Seoul National University 저자: Eunjeong Park, Department of Electrical Engineering and Computer Science, INMC, Seoul National University 저자: Dongyeon Woo, Department of Electrical Engineering and Computer Science, INMC, Seoul National University 저자: Hyung-Sin Kim, Department of Electrical Engineering and Computer Science, University of California 저자: Jongyeup Paek, Department of Computer Science and Engineering, Chung-Ang University 저자: Saewoong Bahk, Department of Electrical Engineering and Computer Science, INMC, Seoul National University
With the proliferation of emerging Internet of Things(IoT) devices and applications, mobility is becoming an integralpart of low-power and lossy networks (LLNs). However, most LLNprotocols have not yet focused on the support for mobility with anexcuse of resource constraints. Some work that do provide mobilitysupport fail to consider radio duty-cycling, control overhead,or memory usage, which are critical on resource-limited lowpowerdevices. In this paper, we introduce MAPLE, an asymmetrictransmit power-based routing architecture that leverages a singleresource-rich LLN border router. MAPLE supports mobility induty-cycled LLNs using received signal strength indicator (RSSI)gradient field-based routing. High-power transmission of the gatewaynot only allows LLN endpoints to be synchronized for lowduty-cycle operation, but also establishes an RSSI gradient fieldwhich can be exploited for opportunistic routing without a needfor any neighbor or routing table. This eliminates the scalabilityproblem due to memory limitation, and provides a responsiverouting metric without control overhead. MAPLE also addressesthe RSSI local maximum problem through local adaptation. Weimplement MAPLE on a low-power embedded platform, and evaluatethrough experimental measurements on a real multihop LLNtestbed consisting of 31 low-power ZigBee nodes and 1 high-powergateway. We show that MAPLE improves the performance of mobiledevices in LLN by 27.2%/55.7% and 17.9% in terms of bothuplink/downlink reliability and energy efficiency, respectively.
